Dynamic shape-morphing soft materials systems are ubiquitous in living organisms; they are also of rapidly increasing relevance to emerging technologies in soft machines, flexible electronics and smart medicines. Soft matter equipped with responsive components can switch between designed shapes or structures, but cannot support the types of dynamic morphing capabilities needed to reproduce natural, continuous processes of interest for many applications. Challenges lie in the development of schemes to reprogram target shapes after fabrication, especially when complexities associated with the operating physics and disturbances from the environment can stop the use of deterministic theoretical models to guide inverse design and control strategies. Here we present a mechanical metasurface constructed from a matrix of filamentary metal traces, driven by reprogrammable, distributed Lorentz forces that follow from the passage of electrical currents in the presence of a static magnetic field. The resulting system demonstrates complex, dynamic morphing capabilities with response times within 0.1 second. Implementing an in situ stereo-imaging feedback strategy with a digitally controlled actuation scheme guided by an optimization algorithm yields surfaces that can follow a self-evolving inverse design to morph into a wide range of three-dimensional target shapes with high precision, including an ability to morph against extrinsic or intrinsic perturbations. These concepts support a data-driven approach to the design of dynamic soft matter, with many unique characteristics.
黄永刚美国国家工程院院士与科学院院士 中国科学院外籍院士 英国皇家学会院士
Prof. Yonggang Huang has been working on mechanics of materials and structures across multiple scales, such as the mechanism-based strain gradient plasticity theory, and atomistic-based continuum theory for carbon nanotubes. In recent years he has focused on mechanics and thermal analysis of stretchable and dissolvable electronics with applications to energy harvesting and medicine, and mechanically guided, deterministic 3D assembly. His work on the electronic tattoos has been reported by NBC Learn (the education arm of NBC).